Clustering-based weighted extreme learning machine for classification in drug discovery process

Kudisthalert, Wasu; Pasupa, Kitsuchart

doi:10.1007/978-3-319-46687-3_49

Clustering-based weighted extreme learning machine for classification in drug discovery process

Author(s)

Kudisthalert, Wasu

Pasupa, Kitsuchart

Date Issued

January 1, 2016

Type

Conference Paper

DOI

10.1007/978-3-319-46687-3_49

Abstract

Extreme Learning Machine (ELM) is a universal approximation method that is extremely fast and easy to implement, but the weights of the model are normally randomly selected so they can lead to poor prediction performance. In this work, we applied Weighted Similarity Extreme Learning Machine in combination with Jaccard/Tanimoto (WELM-JT) and cluster analysis (namely, k-means clustering and Support Vector Clustering) on similarity and distance measures (i.e., Jaccard/ Tanimoto and Euclidean) in order to predict which compounds with not-so-different chemical structures have an activity for treating a certain symptom or disease. The proposed method was experimented on one of the most challenging datasets named Maximum Unbiased Validation (MUV) dataset with 4 different types of fingerprints (i.e. ECFP 4, ECFP 6, FCFP 4 and FCFP 6). The experimental results show that WELM-JT in combination with k-means-ED gave the best performance. It retrieved the highest number of active molecules and used the lowest number of nodes. Meanwhile, WELM-JT with k-means-JT and ECFP 6 encoding proved to be a robust contender for most of the activity classes.

Citation

Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 9947 LNCS, 441-450, 2016